CN111513726B - System for evaluating AMS risk based on IHT dynamic performance - Google Patents

Abstract

The invention provides a system for evaluating AMS risk based on IHT dynamic performance, which comprises a data acquisition module, a data processing module, a robust enhancement module and an AMS severity monitor, wherein the data acquisition module is used for acquiring data; the data acquisition module acquires SpO from a plurality of continuous IHT periods₂Information; data processing module pair obtained SpO₂Processing abnormal values, filtering and dividing data segments of the information, and selecting low-oxygen data segments; the index acquisition module acquires a dynamic blood oxygen saturation index DSI of the hypoxia data segment, wherein the DSI is SpO₂Duration of holding above a set ratio, denoted as τ_i(ii) a Robust enhancement module for the dynamic blood oxygen saturation index tau_iCarrying out robustness enhancement to obtain an index tau_r(ii) a AMS severity monitor based on a predetermined reference value

And said τ_rAnd comparing to realize evaluation on the AMS. The invention provides a novel AMS (automatic reference system) prediction index DSI (dynamic stress indicator) system by utilizing a dynamic process of human hypoxia stress reaction reflected by continuous physiological information in IHT (IHT).

Description

System for evaluating AMS risk based on IHT dynamic performance

Technical Field

The invention relates to a system for evaluating AMS risk based on IHT dynamic performance, and belongs to the technical field of prediction and evaluation of acute altitude diseases.

Background

Acute altitude sickness (AMS) is a particular manifestation of inadequate hypoxia tolerance. This common high altitude reaction condition is caused by the rarefied oxygen and low pressure. Sudden entry into the plateau by a person who is not adapted to the experience of a high-altitude environment can produce related symptoms including headache, insomnia, anorexia, and the like. Although acute altitude disease as a hypoxic stress response helps the body to adapt to changing environments and establish new homeostasis, the sustained response severely affects people's lives in high altitude areas.

In the field of sports, Intermittent Hypoxic Training (IHT) is a very popular training method, which can improve athletic performance of athletes and also alleviate symptoms of acute altitude diseases. IHT adopts a method of intermittently taking low-concentration oxygen to simulate the environment of plateau areas. The method intermittently provides sufficient normal oxygen for body without injury to body. This approach facilitates the establishment of a hypoxia adaptation mechanism, accelerates the adaptation process and reduces the risk of AMS. However, the prediction and evaluation of acute altitude diseases by IHT still face many challenges:

1) the training schemes are various and the results are different. Currently, many studies discuss the effectiveness of IHT and devise training schemes and systems. For example, the intermittent hyperhypoxic training system proposed in document [1] (wanbosu, olygger thanks glazakhstaff, chenkuai, guo, huxiazhou. intermittent hyperhypoxic training system [ P ]. CN110575596A, 2019-12-17.). However, these training protocols differ greatly in hypoxia duration and oxygen concentration, and the experimental conclusions drawn from different IHT studies also differ. Document [2] (T.O' Connor, G.Dubowitz, P.E.Bickler.pulse oxidation in the diagnosis of acid methyl acetic acid assay [ J ]. High Altitude Medicine & Biology,2004,5(3),341 & 348) and document [3] (S.Grant S, N.MacLeod, J.W.Kay, M.Watt, S.Patel, A.Paterson, A.Peacock.Sea. level and acid responses to a. do the y compression physiological responses and acid methyl acetic acid assay kit [ J ]. weigh Journal of Spur, 2002,36 (2)), the results of the same type of assay were analyzed using the same criteria, but opposite criteria. Generally speaking, high-intensity hypoxic training results in poor body adaptation, increasing the risk of acute altitude disease, while low-intensity training may result in less than satisfactory training.

2) The subjectivity of the existing diagnosis method of the acute altitude disease. The Louis Lake Score (Lake Louis Score, LLS) is considered an effective method for diagnosing AMS. LLS investigates the severity of five symptoms (including headache, gastrointestinal symptoms, fatigue or weakness, dizziness or vertigo, and difficulty in falling asleep) in a subject by evaluating questionnaires with symptom scoring scales. According to the internationally widely used criteria, subjects with an LLS score greater than 3 were diagnosed with acute altitude disease. However, this diagnostic method is subjective and will be misdiagnosed if the tester denies the associated symptoms.

3) Static physiological indices are difficult to predict accurately. In addition to the most common LLS, some static physiological indicators can also evaluate AMS. In document [4 ]](M.Burtscher,M.Philadelphy,H.Gatterer,J.Burtscher,M.Faulhaber,W.Nachbauer,and R.Likar,Physiological Responses in Humans Acutely Exposed to High Altitude(3480m):Minute Ventilation and Oxygenation Are Predictive for the Development of Acute Mountain Sickness[J].High Altitude Medicine&Biology,2019,20(2),192-₂) The physiological indicators considered to be the most promising candidates for AMS prediction. The Heart Rate (HR) and its variation (HVR) were also used to assess AMS. However, the static index information is highly refined and difficult to reflect dynamic processes.

4) The conventional prediction method is difficult to accurately predict AMS in real time. Document [5] (zhanshui, wikunlun, liuchunxiang, muxin, Yanshi, Zhao Xiaojing, Shijinlong, Jiazhilong, Jiaqian. a plateau adaptability evaluation method and system [ P ]. CN110335678A,2019-10-15.) utilize sample data and doctor experience to establish a plateau adaptability deep neural network evaluation model. The method is influenced by factors such as training samples and the like, and the accuracy is difficult to guarantee. Document [6] (yellow river, Gaoyuan, Liubao, Xugang, Sun Binda.) A kit for predicting the risk of acute mountain sickness by combining four microRNA biomarkers [ P ]. CN107058471A,2017-08-18 ]) utilizes the biomarkers to predict the risk of AMS, and the risk of AMS is difficult to monitor in real time.

The existing indexes for evaluating the severity of the acute altitude disease mainly comprise a subjective index LLS and a static physiological index SpO₂And the like. LLS is currently the urgency of international recognitionThe diagnosis standard of the sexual altitude disease is widely applied, but is limited by long evaluation period and subjectivity, and is difficult to diagnose and evaluate accurately in time. Static physiological indicators, but containing highly refined hypoxic stress response information, are susceptible to disturbances that are difficult to avoid in experiments. Subject to differences in experimental design, subject selection criteria, poster height, etc., it is also possible to reach opposite diagnostic conclusions. Therefore, the dynamic process of human hypoxia stress response reflected by the continuous physiological information of IHT is utilized, the index which effectively reflects the severity of acute altitude diseases is designed, and the method for constructing real-time monitoring and performance evaluation has important significance for AMS prediction.

Disclosure of Invention

The invention aims to overcome the defects of the existing indexes and provide a system for evaluating the risk of AMS (advanced coronary artery disease) based on IHT (acute respiratory syndrome) dynamic performance.

The invention is realized by the following technical scheme:

a system for evaluating AMS risk based on IHT dynamic performance comprises a data acquisition module, a data processing module, a robust enhancement module and an AMS severity monitor; wherein the content of the first and second substances,

a data acquisition module for acquiring SpO from multiple consecutive IHT periods₂Information;

a data processing module for processing the acquired SpO₂Processing abnormal values, filtering and dividing data segments of the information, and selecting low-oxygen data segments;

an index obtaining module, configured to obtain a dynamic blood oxygen saturation index DSI of the hypoxia data segment, where the DSI is SpO₂Duration of holding above a set ratio, denoted as τ_i；

A robust enhancement module for the dynamic blood oxygen saturation indicator tau_iCarrying out robustness enhancement to obtain an index tau_r；

AMS severity monitor based on a predetermined reference value

And said τ_rMake a comparison to satisfy

The people who train are qualified and suitable for entering the plateau area to meet

To be further trained and observed, to thereby enable assessment of AMS risk.

Further, the abnormal value of the present invention is processed as follows: and processing by adopting a method of replacing the abnormal value with a non-zero data average value before and after the abnormal value.

Further, the criteria for selecting hypoxic data segments according to the invention are: first, the length of the hypoxic data segment is greater than a preset value_minSecond, remove the average SpO of the last n samples₂Hypoxic segments at levels greater than 90%.

Further, the preset l of the present invention_minIs 250 seconds, and the value of n is 200 seconds.

Further, the dynamic blood oxygen saturation index tau of the hypoxia data segment of the invention_iIs SpO₂For a duration of more than 85%.

Further, the robustness of the invention is enhanced as follows:

ordering DSI sequence collected in one IHT period according to size, namely { tau_iI 1.. lambda., lambda } where lambda represents the number of suitable hypoxic data segments when i is equal to 1<j is, τ_i≤τ_j(ii) a DSI acquired in one IHT period is defined as a sequence { tau_iThe twenty-th percentile of i 1, λ, is denoted as τ_r。

Further, the present invention should be taken as { τ [ ]_iWhen the base number of i ═ 1.. lambda. } is smaller than a set value, { τ } is estimated by the following method_iI 1, a second of λA ten percentile; the method specifically comprises the following steps:

let τ be_iHas a maximum value of

Minimum value of

Will tau_iRange of (1)

Divided into sub-intervals of length l of delta segment

Wherein, pi_j(j ═ 1, 2.. multidot., δ) is τ _i1,2, λ falls in the jth subinterval, the twentieth locus falls in the subinterval z^*Internal;

s.t.,g(z)≥0

wherein

The area of the first 20% of the total histogram area and the first z-1 segment

The difference between the two;

let Pi ₀0, the DSI of a hypoxic training period is defined as τ_r

τ_r：＝τ+l_τ

Further, the present inventionThe risk assessment module assesses: reference value

LLS is used as a classification label and is obtained according to the maximum classification accuracy criterion.

Further, the reference value of the present invention

Is 210 seconds.

Advantageous effects

The invention provides a novel AMS (automatic reference System) prediction index DSI (dynamic stress indicator) by utilizing a dynamic process of human hypoxia stress reaction reflected by continuous physiological information in IHT (IHT) under the framework of process monitoring and performance evaluation. The present invention also determines criteria for classifying the hypoxic environment-adapted and non-hypoxic environment-adapted training subjects using DSI.

(1) The most outstanding characteristic of the AMS prediction index DSI is to introduce the thought of process monitoring and performance evaluation in the control theory field and analyze SpO in IHT₂And continuously monitoring information, and obtaining an AMS prediction index by using the dynamic characteristics of the information. Compared with static indexes, the method contains more abundant information and can realize real-time monitoring and evaluation.

(2) The method provided by the invention utilizes methods such as filtering and the like to enable the provided index to have certain robustness on disturbance such as measurement noise, uncertainty factors in a training process and the like.

(3) According to the invention, the individual does not need to enter a plateau environment, the DSI can also predict the severity of the acute altitude sickness, and the convenience and flexibility of actual monitoring are enhanced.

Drawings

FIG. 1 is a block diagram of a dynamic monitoring and performance evaluation system for acute altitude disease prediction based on intermittent hypoxia training according to an embodiment of the present invention;

FIG. 2 is a schematic diagram of a data preprocessing method according to an embodiment of the present invention;

FIG. 3 is a diagram of the low oxygen data segment DSI according to an embodiment of the present invention;

FIG. 4 is a diagram of a DSI robustness enhancement algorithm for an IHT period according to an embodiment of the present invention;

FIG. 5 is a least squares fit plot of DSI metrics of all trainees and physiological metrics measured in the hypoxic chamber in an embodiment of the invention;

FIG. 6 is a DSI comparison before and after intermittent hypoxia training in an embodiment of the present invention;

FIG. 7 is a least squares fit graph of DSI indicators of an intermittent hypoxia training susceptible person and physiological indicators measured in a hypoxic chamber in an embodiment of the invention.

Detailed Description

The invention is described in further detail below with reference to the following detailed description and accompanying drawings: the present example is carried out on the premise of the technical solution of the present invention, but the scope of the present invention is not limited to the following embodiments.

The design idea of the invention is as follows: AMS severity is researched and predicted by utilizing physiological data dynamic characteristics of IHT continuous monitoring process, and SpO obtained from IHT₂The information is subjected to data preprocessing (including abnormal value processing, filtering and data segment division) to obtain a Dynamic blood oxygen saturation indicator (Dynamic SpO) of each hypoxia period₂Index, DSI) and robustness enhancement, using this Index to predict the risk of the trainee to develop AMS, and finally, designing an AMS severity monitor (classifier) for determining whether the individual is suitable for the plateau environment.

A system for evaluating AMS risk based on IHT dynamic performance comprises a data acquisition module, a data processing module, a robust enhancement module and an AMS severity monitor; as shown in fig. 1, in which,

a data acquisition module for acquiring SpO from multiple consecutive IHT periods₂And (4) information.

In this embodiment, for training and pre-adaptation, the data acquisition module acquires SpO from multiple consecutive IHT cycles₂Information, { s { S }^j(k):k＝1,...,l^jRepresents the SpO obtained in the j-th IHT period₂The original data sequence of (1).

A data processing module for processing the acquired SpO₂And carrying out abnormal value processing, filtering and data segment division on the information, and selecting a low-oxygen data segment.

Abnormal values caused by external environments such as instrument and equipment in training are reasonably screened and removed, and high-frequency noise of a sampling signal is filtered. Meanwhile, in the IHT, an oxygen concentration in the form of a periodic square wave is supplied. SpO trainer₂The level fluctuates with fluctuations in oxygen concentration. Since the present example focuses on SpO at low oxygen concentrations₂The high concentration oxygen section data is removed and only the low concentration oxygen section data is studied. Has a length of

Is defined as

A data segment of low oxygen concentration. The last step in the data segment partitioning is to select data segments that meet the following criteria:

the first criterion specifies that the length of the hypoxic data segment is greater than l_minShort length data segments are difficult to adequately describe hypoxic stress response. Second criterion removes average SpO of the last n sampling points₂Hypoxic segments at levels greater than 90% thus rule out data aberrations caused by deep breathing. To avoid confusion, the number of suitable hypoxic data segments is represented by λ.

An index obtaining module, configured to obtain a dynamic blood oxygen saturation index DSI of the hypoxia data segment, where the DSI is SpO₂Duration of holding above a set ratio, denoted as τ_i。

To assess the capacity of the human hypoxia stress response system, the present embodiment is based on the rise time T_rThe concept of (a) proposes an index. The rise time being a deterministic control performance evaluationAnd the index reflects the response speed of the dynamic system following the step input. Consider a dynamic system

Where u is the external input, x is the system internal state, and y is the output. Assuming the system can reach steady state, the rise time T_rDefined as the time it takes for the first change in the system response y (t) from 10% to 90% of the steady state value. The shorter the rise time, the faster the system will respond to the step input.

In IHT, the oxygen concentration is used as input u, SpO₂The hypoxia stress response system with level as output y is asymptotically stable. In IHT, the oxygen concentration varies with a pre-designed periodic square wave, and once the oxygen concentration decreases, the SpO of the human body₂The level is reduced until the SpO in the simulated plateau environment is reached within a certain time₂A steady state value. Although there are physiological differences in humans, the mean steady state value fluctuates at a level of 75% to 80%. Is denoted as τ_iI.e. a reasonable hypoxic data segment

The DSI of (1). In an IHT hypoxia data segment, tau_iIs SpO₂For a duration of more than 85%. Tau is_iIs defined mathematically as

Sampling period of T_s。

A robust enhancement module for the dynamic blood oxygen saturation indicator tau_iCarrying out robustness enhancement to obtain an index tau_r。

SpO₂High sensitivity and low accuracy of (2) are prone to erroneous predictions. Therefore, it is necessary to enhance the robustness of the proposed index. The invention sorts DSI sequences collected in an IHT period according to size, namely { tau_iI 1.. lambda.l.. When i is<j is, τ_i≤τ_j. DSI of one IHT period is defined as the sequence { tau }_iI-the twentieth percentile of 1. If { τ_iWhen the base of i 1.. lambda.. is smaller than a predetermined value (e.g., smaller than 10), we estimate { τ using the following method_iI-the twentieth percentile of 1.

From the viewpoint of robustness, the estimation method is based on tau_iThe area of the frequency distribution histogram of (4) is obtained. Let τ be_iHas a maximum value of

Minimum value of

Will tau_iRange of (1)

Divided into sub-intervals of length l of delta segment

π_j(j ═ 1, 2.. multidot., δ) is τ_iI 1,2, λ falls in the jth subinterval. The twentieth percentile falls within the subinterval z^*And (4) the following steps.

s.t.,g(z)≥0

Wherein

For the first 20% of the total histogram area and the first z-1 segmentArea of

The difference between them. To facilitate the calculation, let π ₀0. Finally, the DSI of a hypoxic training period is defined as τ_r

τ_r：＝τ+l_τ

If it is not

Training for evaluating an IHT cycle may be less accurate and selected closerτThe value of (A) will also be influenced by SpO₂In the vicinity of 85%, the present embodiment calculates DSI of one IHT period by using the above method, in consideration of these two reasons, thereby enhancing the robustness of the index. This process is schematically illustrated in fig. 4.

AMS severity monitor based on a predetermined reference value

And said τ_rMake a comparison to satisfy

The people who train are qualified and suitable for entering the plateau area to meet

Is classified as an unadapted individual for further training observation.

Specifically, the method comprises the following steps: according to the reference value

AMS severity monitors (classifiers) were designed. This monitor (classifier) is used to determine whether an individual is in a high altitude environment during IHT. Satisfy the requirement of

Is well-trained and suitable for entering plateau areas, however

Is classified as an unadapted individual for further training observation. Selection by DSI relationship to LLS

LLS is used as a classification label and corresponding classification benchmark is obtained according to the maximum classification accuracy criterion

This criterion was used as a basis to classify people with AMS from those who do not.

The following example demonstrates the correlation of IHT data with AMS indicators, and the IHT data can be processed to detect AMS.

First, 18 subjects were subjected to intermittent hypoxic training. Ten days of continuous intermittent hypoxic training are carried out in a day period (namely, 10 intermittent hypoxic training periods are total). During each training period, the subjects ingested periodically fluctuating concentrations of oxygen, including a low oxygen concentration phase (11% -14% oxygen) lasting 5 minutes and a high oxygen concentration phase (35% -38%) lasting 3 minutes. A high oxygen deficiency device (GO2Altitude hypoxinator) (Biomedtech Australia pty. ltd., Melbourne, Australia) may be employed for intermittent hypoxia training. The hypoxia training device is a mask type device and can provide oxygen with low concentration and high concentration according to the gas separation principle. The device is equipped with a pulse oximeter unit for measuring Heart Rate (HR) and blood oxygen saturation (SpO)₂) Time of sampling T_sIs 1 second.

Second step, collected SpO₂And data preprocessing, including abnormal value processing, filtering and data partitioning.

At SpO₂In monitoring, when the sensor does not collect information, "s (k) ═ 0" appears in successive data points "The case (1). This results in the generation of outliers in the original data set s (k), as shown in fig. 2 (a). This embodiment deals with outliers by replacing them with an average of non-zero data before and after the outliers.

Defined as the ith exception fragment. When k ∈ { α ∈ [ ]_i,β_iH, smoothing the data sequence s₀(k) Is defined as

For normal data points (s (k) ≠ 0), let s₀(k) S (k). The effect of processing the abnormal data is shown in fig. 2 (b).

After processing the abnormal value, the influence of the high-frequency noise is reduced by using a moving average filtering method, and the window width which is the data width participating in the moving average is 50. Length of l_fL- ω +1 post-filtering sequence s_f(k) Is composed of

The filtering effect is shown in fig. 2 (c).

Has a length of

Is defined as

The data segment division criterion is that the length of the hypoxia data segment is more than 250 seconds and the average SpO is 200s after the hypoxia data segment is removed₂Hypoxic segment with a level greater than 90%, namely:

the first criterion specifies that the length of the hypoxic data segment is greater than l_minShort length data segments are difficult to adequately describe hypoxic stress response. Second criterion removes average SpO of the last n sampling points₂Hypoxic segments at levels greater than 90% thus rule out data aberrations caused by deep breathing. To avoid confusion, the number of suitable hypoxic data segments is represented by λ. The data segment division effect is shown in fig. 2 (d).

And thirdly, obtaining DSI trained every day and enhancing the robustness of the DSI.

First, the DSI, i.e., τ, of each hypoxic period segment is determined_iAs shown in fig. 3.

s.t.,η_i(k)≤0.85

The robustness of DSI of one IHT period is evaluated and the DSI sequence { tau } of the period is calculated_iThe twenty-th percentile of i 1.., λ } is used as the period DSI. If { τ_iI 1.. λ } is small, we use the radix in terms of τ_iIs estimated by the area of the frequency distribution histogram of (1) { tau }_iI-1.., λ } in which the histogram subintervals are divided into 15 segments, the calculation of this method is schematically shown in fig. 4.

And fourthly, before and after the hypoxia training, carrying out a hypoxia tolerance test by using the same mask hypoxia training device and obtaining a Hypoxia Tolerance Index (HTI). HTI is SpO of training subjects within 420 seconds₂For a duration of more than 85%. For simplicity, with HTI₁And HTI₂Representing the level of hypoxia tolerance before and after IHT. Defining the difference in the level of hypoxia tolerance before and after training, Δ HTI ═ HTI₂-HTI₁。

Fifth, after the last IHT cycle, the subjects were sent to a low atmospheric pressure at 9pmThe oxygen chamber (air force medical research institute, beijing, china) was used for overnight sleep experiments. The atmospheric hypoxic chamber simulates the atmospheric environment (oxygen concentration is about 12%) in the area with the altitude of 4300 m. The sleep index includes sleep time, deep sleep time and ratio, night wake time, and physiological index such as blood oxygen saturation (SpO)₂) And Heart Rate (HR). The micro-motion sensitive mattress sleep detection system RS-611 (emerging Yang Sheng technology Co., Ltd., Beijing, China) is used for sleep monitoring. The system can make accurate measurements without affecting normal sleep. The next day, 7am, the training subjects were subjected to the Louisis Lake (LLS) test.

Sixthly, by using LLS as a classification label and according to the maximum classification accuracy criterion, the corresponding data can be obtained from the data in the first sub-graph in FIG. 7

Is 210 seconds.

This example analyzed the correlation between AMS prediction index derived from IHT data and physiological index measured in hypoxic chamber, the analysis results are shown in Table 1, and Table 1 shows the correlation between DSI index of all trainers in this example and physiological index measured in hypoxic chamber.

Table 1: DSI, MSHS, HTI of tenth IHT period₁、HTI₂Delta HTI and correlation results of physiological indices measured in hypoxic chamber (n ═ 18)

A p-value < 0.05 (two-tailed) is considered statistically significant for the correlation analysis. SD represents sleep time (sleep duration); DSD denotes deep sleep duration (deep sleep duration); DSR denotes a deep sleep rate (deep sleep rate); DNA represents wake time (duration of night awaking); MSDS represents SpO while sleeping₂Mean value (mean SpO)₂during sleep); MHDS represents the mean HR reducing sleep mean value (mean HR reducing sleep); MSHS represents low oxygen segment SpO₂Mean value (mean SpO)₂ of hypoxic segments)

On the one hand, HTI was experimentally observed₁Related to deep sleep time (r 0.515, p 0.029), Δ HTI and wake time (r 0.568, p 0.014). In addition to this, HTI₁、HTI₂Δ HTI and mean SpO₂There was no significant correlation with the physiological indicators measured in hypoxic chambers. On the other hand, experiments have observed that three important indicators characterizing the risk of AMS are included: deep sleep time (r 0.492, p 0.038), deep sleep ratio (r 0.623, p 0.006) and average blood oxygen saturation (r 0.525, p 0.025). The effectiveness of DSI indicators in predicting AMS risk is therefore validated. Unfortunately, there was no significant correlation between the AMS predictor and the LLS indicator based on the data of the entire study population. One possible reason is that the LLS is judged based on subjective knowledge, judgment and willingness of the individual being evaluated. In addition, the least squares fit results of the correlation study are shown in fig. 5. These fitted curves show the data distribution trend well. A DSI contrast plot before and after intermittent hypoxic training (i.e., the first training period and the tenth training period) is shown in fig. 6. This figure illustrates that intermittent hypoxia training helps to improve the adaptive capacity of the individual's hypoxic environment.

When 12 training subjects with Δ HTI > 30 were studied in a centralized manner, the correlation between DSI and physiological measures in hypoxic chamber are shown in table 2, and table 2 shows the results of the analysis of the correlation between DSI measures of the intermittent hypoxic training sensitizers in this example and physiological measures in hypoxic chamber.

Table 2: correlation results between DSI of the tenth IHT cycle and physiological indices measured in hypoxic chamber (n 12)

A p-value < 0.05 (two-tailed) is considered statistically significant for the correlation analysis. SD represents sleep time (sleep duration); DSD denotes deep sleep duration (deep sleep duration); DSR denotes a deep sleep rate (deep sleep rate); DNA represents wake time (duration of night awaking); MSDS represents SpO while sleeping₂Mean value (mean SpO)₂ during sleep)；MHDS represents the mean HR sleep time (mean HR reducing sleep)

The correlations between DSI and partial physiological indicators of the tenth training period are improved, including the deep sleep time (r 0.907, p < 0.001), the deep sleep ratio (r 0.783, p 0.003) and the average blood oxygen during sleep (r 0.730, p 0.007), DSI and LLS also have significant correlations (r 0.622, p 0.031), the least squares fit curve of the results is shown in fig. 7. Since HTI also has a role in predicting risk of AMS, Δ HTI reflects to some extent the individual's adaptability or sensitivity to hypoxia. Therefore, the proposed DSI index has better prediction effect on patients sensitive to intermittent hypoxia training (namely individuals with enhanced adaptability to hypoxia environment after training).

In summary, the above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (7)

1. A system for evaluating AMS risk based on IHT dynamic performance is characterized by comprising a data acquisition module, a data processing module, a robust enhancement module and an AMS severity monitor; wherein the content of the first and second substances,

a data acquisition module for acquiring SpO from multiple consecutive IHT periods₂Information;

a data processing module for processing the acquired SpO₂Processing abnormal values, filtering and dividing data segments of the information, and selecting low-oxygen data segments;

an index obtaining module, configured to obtain a dynamic blood oxygen saturation index DSI of the hypoxia data segment, where the DSI is SpO₂Duration of holding above a set ratio, denoted as τ_i；

A robust enhancement module for the dynamic blood oxygen saturation indicator tau_iCarrying out robustness enhancement to obtain an index tau_r；

AMS severity monitor based on a predetermined reference value

And said τ_rMake a comparison to satisfy

The people who train are qualified and suitable for entering the plateau area to meet

People classified as non-adapted individuals are required to be further trained and observed, so that evaluation on AMS risk is realized;

the robustness enhancement is as follows:

ordering DSI sequence collected in one IHT period according to size, namely { tau_iI 1.. lambda., lambda } where lambda represents the number of suitable hypoxic data segments when i is equal to 1<j is, τ_i≤τ_j(ii) a DSI acquired in one IHT period is defined as sequence

The twentieth percentile of (d) is recorded as τ_r；

When { tau_iWhen the base number of i ═ 1.. lambda. } is smaller than a set value, { τ } is estimated by the following method_iThe twenty-th percentile of i ═ 1.., λ }; the method specifically comprises the following steps:

let τ be_iHas a maximum value of

Minimum value of

Will tau_iRange of (1)

Divided into sub-intervals of length l of delta segment

Wherein, pi_j(j ═ 1, 2.. multidot., δ) is τ_i1,2, λ falls in the jth subinterval, the twentieth locus falls in the subinterval z^*Internal;

s.t.,g(z)≥0

wherein

The area of the first 20% of the total histogram area and the first z-1 segment

The difference between the two;

let Pi₀0, the DSI of a hypoxic training period is defined as τ_r

τ_r：＝τ+l_τ

2. The system for evaluating AMS risk based on IHT dynamic performance of claim 1, wherein the outliers are processed as: and processing by adopting a method of replacing the abnormal value with a non-zero data average value before and after the abnormal value.

3. The system for assessing AMS risk based on IHT dynamic performance of claim 1, wherein the criteria for selecting the hypoxic data segment are: first, the length of the hypoxic data segment is greater than a preset value_minSecond, remove the average SpO of the last n samples₂Hypoxic segments at levels greater than 90%.

4. The system for assessing AMS risk based on IHT dynamic performance of claim 3, wherein the preset/is_minIs 250 seconds, and the value of n is 200 seconds.

5. The system for assessing AMS risk based on IHT dynamics of claim 1, wherein the dynamic oximetry indicator τ of the hypoxic data segment_iIs SpO₂For a duration of more than 85%.

6. The system for evaluating AMS risk based on IHT dynamic performance of claim 1, wherein the risk evaluation module evaluates: reference value

LLS is used as a classification label and is obtained according to the maximum classification accuracy criterion.

7. The system for assessing AMS risk based on IHT dynamic performance of claim 1, wherein the baseline value is

Is 210 seconds.

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